Probabilistic Instruction Cache Analysis Using Bayesian Networks

Bartlett, Mark; Bate, Iain; Cussens, James; Kazakov, Dimitar

doi:10.1109/rtcsa.2011.55

Cited by 2 publications

(4 citation statements)

References 15 publications

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“…An earlier version of this technique has been previously evaluated against the program with the control flow graph shown in figure 1 and found to correspond reasonably to the ideal Bayesian network, albeit with some overfitting [10]. As the bubblesort is a real example for which an ideal Bayesian network was not known and very difficult to derive, it is impossible to evaluate the network based on how closely it corresponds to the ideal network.…”

Section: Results and Evaluationmentioning

confidence: 99%

“…The method presented here expands considerably on a technique that has been presented elsewhere [10] and develops that previous approach to increase the accuracy of the learned networks.…”

Section: Instruction Cache Analysis Using Bayesian Networkmentioning

confidence: 99%

“…The rest of this section now explains how the relevant data is obtained, processed and then used to infer the appropriate Bayesian network. The issue of how this network can be used to estimate the number of cache misses and WCET is addressed in [10].…”

Section: Instruction Cache Analysis Using Bayesian Networkmentioning

confidence: 99%

See 2 more Smart Citations

Learning Bayesian Networks for Improved Instruction Cache Analysis

Bartlett

Bate

Cussens

2010

2010 Ninth International Conference on Machine Learning and Applications

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Abstract-As modern processors can execute instructions at far greater rates than these instructions can be retrieved from main memory, computer systems commonly include caches that speed up access times. While these improve average execution times, they introduce additional complexity in determining the Worst Case Execution Times crucial for Real-Time Systems. In this paper, an approach is presented that utilises Bayesian Networks in order to more accurately estimate the worst-case caching behaviour of programs. With this method, a Bayesian Network is learned from traces of program execution that allows both constructive and destructive dependencies between instructions to be determined and a joint distribution over the number of cache hits to be found. Attention is given to the question of how the accuracy of the network depends on both the number of observations used for learning and the cardinality of the set of potential parents considered by the learning algorithm.

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Section: Results and Evaluationmentioning

confidence: 99%

“…The method presented here expands considerably on a technique that has been presented elsewhere [10] and develops that previous approach to increase the accuracy of the learned networks.…”

Section: Instruction Cache Analysis Using Bayesian Networkmentioning

confidence: 99%

See 1 more Smart Citation

Learning Bayesian Networks for Improved Instruction Cache Analysis

Bartlett

Bate

Cussens

2010

2010 Ninth International Conference on Machine Learning and Applications

Self Cite

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“…For this purpose, Maximum Weight Spanning Tree (MWST) algorithm is more efficient, which diverted the focus from BN to MWST as well as more dependency on manual intervention. Milns et al [75] and Bartlett et al [76] presented their BN based ecological network and probabilistically cache analysis respectively.…”

Section: Bayesian Network Applications Miscellaneous Domainsmentioning

confidence: 99%

An Overview of Bayesian Network Applications in Uncertain Domains

Iqbal¹,

Yin²,

Hao³

et al. 2015

IJCTE

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Abstract-Uncertainty is a major barrier in knowledge discovery from complex problem domains. Knowledge discovery in such domains requires qualitative rather than quantitative analysis. Therefore, the quantitative measures can be used to represent uncertainty with the integration of various models. The Bayesian Network (BN) is a widely applied technique for characterization and analysis of uncertainty in real world domains. Thus, the real application of BN can be observed in a broad range of domains such as image processing, decision making, system reliability estimation and PPDM (Privacy Preserving in Data Mining) in association rule mining and medical domain analysis. BN techniques can be used in these domains for prediction and decision support. In this article, a discussion on general BN representation, draw inferences, learning and prediction is followed by applications of BN in some specific domains. Domain specific BN representation, inferences and learning process are also presented. Building upon the knowledge presented, some future research directions are also highlighted.Index Terms-Uncertainty, knowledge discovery, Bayesian network, image processing, decision making, privacy preservation, system reliability estimation. I. INTRODUCTIONUncertainty is a commonly faced problem in real world applications. Uncertainty can be described as an inadequate amount of information [1]. Nevertheless, uncertainty may also exist in situations that have enough amount of information [2]. Furthermore, uncertainty may be alleviated or eliminated with the addition of new information. Addition of more information in complex processes may lead to mining of limited knowledge. Uncertainty can be computed mathematically with probability theory. In uncertain situations, there is an involvement of possibility of states of attributes. Consequently, the models established on probabilistic inferences have the capability to assign a probabilistic value according to a defined principle. Accordingly, the prediction with large number of states in a model is accomplished. The question rises "how prediction is realized in the presence of large number of states in a model?" An answer to this question is the employment of Bayesian Network (BN) with several variables [3]- [5]. BNs, also known as belief networks, belong to the family of probabilistic graphical models. These graphical structures correspond to knowledge about an uncertain domain. More specifically, each node in the graphical structure represents a random variable, while the edges/arcs between the nodes represent conditional dependencies among nodes. These conditional dependencies are estimated by using acknowledged statistical and computational methods. Consequently, BNs incorporate concepts from graph and probability theory, computer science, and statistics.Since last two decades, BN is recognized as an important tool for a number of expert systems especially in domains involving uncertainty [6]. This recognition of BN has several reasons behind it. First, BN encodes the depen...

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Probabilistic Instruction Cache Analysis Using Bayesian Networks

Cited by 2 publications

References 15 publications

Learning Bayesian Networks for Improved Instruction Cache Analysis

Learning Bayesian Networks for Improved Instruction Cache Analysis

An Overview of Bayesian Network Applications in Uncertain Domains

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